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by polydev-ai
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JavaScript
Hybrid

Ask models (old name)

get_perspectives
Read-only

Ask multiple AI models in parallel to get diverse perspectives on a prompt, code, or error. Compare models side by side to validate reasoning and pick the best answer.

Instructions

Old name of ask_model. Same inputs and the same behaviour.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "prompt": "<prompt>"
    },
    "name": "get_perspectives"
  }
}

Input Schema

NameRequiredDescriptionDefault
modelNoOptional: one model ID to ask, for example a model shown by list_models.
modelsNoOptional: several model IDs to ask. Without model or models, your default set is asked.
promptYesThe prompt to send. Include the relevant code, errors and constraints.
user_tokenNoPolydev token (starts with pd_). Optional when POLYDEV_USER_TOKEN is set or you have run the login tool.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.2/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, openWorldHint=true and destructiveHint=false, so the safety profile is covered. Beyond that the description adds no behavioural detail of its own; it delegates entirely to ask_model's documentation, and says nothing about cost, latency, rate limits, or whether calling it differs at all from the alias target.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two short sentences with zero filler, and the identifying fact (that this is the old name) is front-loaded ahead of the equivalence claim.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a four-parameter tool with no output schema and open-world side effects, the description does not say whether this alias is deprecated, whether it will be removed, or whether it behaves identically in cost and rate limits. An agent is left to infer that it should route to ask_model instead, which is exactly the decision this definition should settle.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so model, models, prompt and user_token are all documented in the schema, including the default-set fallback and the pd_ token format. The description adds only the claim that inputs are identical to ask_model, which is a consistency hint rather than new meaning, so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states plainly that this tool is the legacy name of ask_model with identical inputs and behaviour, which lets an agent identify it as a duplicate rather than a distinct capability. It does not itself say what asking models does, so the agent must resolve it through the sibling ask_model.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Naming ask_model as the current form strongly implies this should not be the tool of choice, but the description never says so explicitly, gives no deprecation or removal guidance, and never tells the agent to prefer ask_model when both are available.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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